scholarly journals Assessment of the High-Resolution Rapid Refresh Model’s Ability to Predict Mesoscale Convective Systems Using Object-Based Evaluation

2015 ◽  
Vol 30 (4) ◽  
pp. 892-913 ◽  
Author(s):  
James O. Pinto ◽  
Joseph A. Grim ◽  
Matthias Steiner
Keyword(s):  
United States ◽  
High Resolution ◽  
Size Distribution ◽  
Great Plains ◽  
Southeastern United States ◽  
Mesoscale Convective Systems ◽  
Eastern United States ◽  
Convective Systems ◽  
Object Based ◽  
Mesoscale Convective

Abstract An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model’s skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time–dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model’s tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity.

Regional, Seasonal, and Diurnal Variations of Cloud-to-Ground Lightning with Large Impulse Charge Moment Changes

2014 ◽  
Vol 142 (10) ◽  
pp. 3666-3682 ◽  
Author(s):  
Nick K. Beavis ◽  
Timothy J. Lang ◽  
Steven A. Rutledge ◽  
Walter A. Lyons ◽  
Steven A. Cummer
Keyword(s):  
United States ◽  
Atlantic Ocean ◽  
Great Plains ◽  
Southeastern United States ◽  
Northern Great Plains ◽  
Total Charge ◽  
Central Plains ◽  
Convective Systems ◽  
Seasonal And Diurnal Variations ◽  
Mesoscale Convective

Abstract The use of both total charge moment change (CMC) and impulse charge moment change (iCMC) magnitudes to assess the potential of a cloud-to-ground (CG) lightning stroke to induce a mesospheric sprite has been well described in the literature, particularly on a case study basis. In this climatological study, large iCMC discharges for thresholds of >100 and >300 C km in both positive and negative polarities are analyzed on a seasonal basis. Also presented are local solar time diurnal distributions in eight different regions covering the lower 48 states as well as the adjacent Atlantic Ocean, including the Gulf Stream. The seasonal maps show the predisposition of large positive iCMCs to dominate across the northern Great Plains, with large negative iCMCs favored in the southeastern United States year-round. During summer, the highest frequency of large positive iCMCs across the upper Midwest aligns closely with the preferred tracks of nocturnal mesoscale convective systems (MCSs). As iCMC values increase above 300 C km, the maximum shifts eastward of the 100 C km maximum in the central plains. Diurnal distributions in the eight regions support these conclusions, with a nocturnal peak in large iCMC discharges in the northern Great Plains and Great Lakes, an early to midafternoon peak in the Intermountain West and the southeastern United States, and a morning peak in large iCMC discharge activity over the Atlantic Ocean. Large negative iCMCs peak earlier in time than large positive iCMCs, which may be attributed to the growth of large stratiform charge reservoirs following initial convective development.

scholarly journals A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

2021 ◽  
Vol 13 (2) ◽  
pp. 827-856
Author(s):  
Jianfeng Li ◽  
Zhe Feng ◽  
Yun Qian ◽  
L. Ruby Leung
Keyword(s):  
United States ◽  
High Resolution ◽  
Atmospheric Chemistry ◽  
Rocky Mountains ◽  
Deep Convection ◽  
The United States ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Data Product ◽  
Mesoscale Convective

Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated Flexible Object Tracker algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by season, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems; quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events; and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

scholarly journals A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

2020 ◽  
Author(s):  
Jianfeng Li ◽  
Zhe Feng ◽  
Yun Qian ◽  
L. Ruby Leung
Keyword(s):  
United States ◽  
High Resolution ◽  
Atmospheric Chemistry ◽  
Rocky Mountains ◽  
Deep Convection ◽  
The United States ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Data Product ◽  
Mesoscale Convective

Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated FLEXTRKR (Flexible Object Tracker) algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by seasons, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems, quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events, and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

scholarly journals Characteristics of Ice Cloud–Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains

2020 ◽  
Vol 21 (2) ◽  
pp. 317-334
Author(s):  
Jingjing Tian ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Zhe Feng
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Great Plains ◽  
Mesoscale Convective Systems ◽  
Spatiotemporal Variability ◽  
Convective Systems ◽  
Ice Cloud ◽  
Convective Core ◽  
Mesoscale Convective ◽  
Ice Water

AbstractIn this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

scholarly journals A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking

2018 ◽  
Vol 57 (7) ◽  
pp. 1599-1621 ◽  
Author(s):  
Alex M. Haberlie ◽  
Walker S. Ashley
Keyword(s):  
United States ◽  
Machine Learning ◽  
Great Plains ◽  
Radar Reflectivity ◽  
Machine Learning Algorithms ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Mesoscale Convective ◽  
Minimization Procedure ◽  
Optimal Approach

AbstractThis research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.

scholarly journals Severe Convective Storms across Europe and the United States. Part I: Climatology of Lightning, Large Hail, Severe Wind, and Tornadoes

2020 ◽  
Vol 33 (23) ◽  
pp. 10239-10261 ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Pieter Groenemeijer ◽  
Roger Edwards ◽  
Harold E. Brooks ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
The United States ◽  
Maximum Activity ◽  
Severe Weather ◽  
Eastern United States ◽  
Convective Storms ◽  
Convective Systems ◽  
Lightning Detection ◽  
Mesoscale Convective

AbstractAs lightning-detection records lengthen and the efficiency of severe weather reporting increases, more accurate climatologies of convective hazards can be constructed. In this study we aggregate flashes from the National Lightning Detection Network (NLDN) and Arrival Time Difference long-range lightning detection network (ATDnet) with severe weather reports from the European Severe Weather Database (ESWD) and Storm Prediction Center (SPC) Storm Data on a common grid of 0.25° and 1-h steps. Each year approximately 75–200 thunderstorm hours occur over the southwestern, central, and eastern United States, with a peak over Florida (200–250 h). The activity over the majority of Europe ranges from 15 to 100 h, with peaks over Italy and mountains (Pyrenees, Alps, Carpathians, Dinaric Alps; 100–150 h). The highest convective activity over continental Europe occurs during summer and over the Mediterranean during autumn. The United States peak for tornadoes and large hail reports is in spring, preceding the maximum of lightning and severe wind reports by 1–2 months. Convective hazards occur typically in the late afternoon, with the exception of the Midwest and Great Plains, where mesoscale convective systems shift the peak lightning threat to the night. The severe wind threat is delayed by 1–2 h compared to hail and tornadoes. The fraction of nocturnal lightning over land ranges from 15% to 30% with the lowest values observed over Florida and mountains (~10%). Wintertime lightning shares the highest fraction of severe weather. Compared to Europe, extreme events are considerably more frequent over the United States, with maximum activity over the Great Plains. However, the threat over Europe should not be underestimated, as severe weather outbreaks with damaging winds, very large hail, and significant tornadoes occasionally occur over densely populated areas.

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